From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations | Paper Citation Record

Observation 984038b7-e018-4f0c-954c-a9be50b91595 · outbound

This paper cites PloS one10(9), 0138146 (2015).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations PloS one10(9), 0138146 (2015)

Reference 1

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This paper cites 5 exposure and risks of ischemic heart disease and stroke events: review and meta-analysis.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations 5 exposure and risks of ischemic heart disease and stroke events: review and meta-analysis

Reference 2

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This paper cites New England Journal of Medicine376(26), 2513–2522 (2017) https://doi.org/10.1056/ NEJMoa1702747 https://www.nejm.org/doi/pdf/10.1056/NEJMoa1702747.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations New England Journal of Medicine376(26), 2513–2522 (2017) https://doi.org/10.1056/ NEJMoa1702747 https://www.nejm.org/doi/pdf/10.1056/NEJMoa1702747

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This paper cites Science of The Total Environment858, 160064 (2023) https: //doi.org/10.1016/j.scitotenv.2022.160064 27.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Science of The Total Environment858, 160064 (2023) https: //doi.org/10.1016/j.scitotenv.2022.160064 27

Reference 4

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This paper cites Environmental science & technology50(1), 79–88 (2016).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environmental science & technology50(1), 79–88 (2016)

Reference 5

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This paper cites American Economic Review114(5), 1338–1381 (2024).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations American Economic Review114(5), 1338–1381 (2024)

Reference 6

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This paper cites Science of The Total Environment571, 416–425 (2016) https://doi.org/10.1016/j.scitotenv.2016.06.213.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Science of The Total Environment571, 416–425 (2016) https://doi.org/10.1016/j.scitotenv.2016.06.213

Reference 7

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This paper cites Environment International199, 109474 (2025) https://doi.org/10.1016/j.envint.2025.109474.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environment International199, 109474 (2025) https://doi.org/10.1016/j.envint.2025.109474

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This paper cites Sustainable Cities and Society54, 101997 (2020) https://doi.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Sustainable Cities and Society54, 101997 (2020) https://doi

Reference 9

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This paper cites Journal of Geophysical Research: Atmospheres 118(4), 2031–2040 (2013) https://doi.org/10.1002/jgrd.50233.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Journal of Geophysical Research: Atmospheres 118(4), 2031–2040 (2013) https://doi.org/10.1002/jgrd.50233

Reference 10

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This paper cites Atmosphere15(12), 1523 (2024).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmosphere15(12), 1523 (2024)

Reference 11

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This paper cites Environmental Monitoring and Assessment198(1), 40 (2026).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environmental Monitoring and Assessment198(1), 40 (2026)

Reference 12

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This paper cites Scientific Reports12(1), 12215 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Scientific Reports12(1), 12215 (2022)

Reference 13

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This paper cites Geoscientific Model Development 10(10), 3695–3713 (2017).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Geoscientific Model Development 10(10), 3695–3713 (2017)

Reference 14

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

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This paper cites In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp

Reference 16

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This paper cites In: 2022 23rd IEEE Inter- national Conference on Mobile Data Management (MDM), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2022 23rd IEEE Inter- national Conference on Mobile Data Management (MDM), pp

Reference 17

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This paper cites Spatio-Temporal Field Neural Networks for Air Quality Inference.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Spatio-Temporal Field Neural Networks for Air Quality Inference

Reference 18

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

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This paper cites Nature Machine Intelligence5(11), 1317–1325 (2023).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Nature Machine Intelligence5(11), 1317–1325 (2023)

Reference 20

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Score-Based Generative Modeling through Stochastic Differential Equations

Reference 21

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This paper cites ACM computing surveys56(4), 1–39 (2023).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ACM computing surveys56(4), 1–39 (2023)

Reference 22

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This paper cites In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp

Reference 23

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This paper cites ISPRS International Journal of Geo-Information15(4), 171 (2026).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ISPRS International Journal of Geo-Information15(4), 171 (2026)

Reference 24

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This paper cites Atmospheric Pollution Research13(5), 101365 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Pollution Research13(5), 101365 (2022)

Reference 25

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This paper cites Nature Machine Intelligence3(11) (2021).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Nature Machine Intelligence3(11) (2021)

Reference 26

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This paper cites In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

Reference 27

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This paper cites Computer Physics Communications308(2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Computer Physics Communications308(2025)

Reference 28

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This paper cites In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks

Reference 29

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This paper cites Advances in neural information processing systems28(2015).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Advances in neural information processing systems28(2015)

Reference 30

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This paper cites European Journal of Operational Research192(3) (2009).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations European Journal of Operational Research192(3) (2009)

Reference 31

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This paper cites Machine Intelligence Research, 1–22 (2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Machine Intelligence Research, 1–22 (2025)

Reference 32

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This paper cites Generative Modelling With Inverse Heat Dissipation.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Generative Modelling With Inverse Heat Dissipation

Reference 33

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This paper cites https:// www.geodair.fr/donnees/consultation.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations https:// www.geodair.fr/donnees/consultation

Reference 34

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations 5 and network activity during extreme pollution events

Reference 35

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This paper cites Atmospheric Environment 41(29), 6116–6131 (2007) https://doi.org/10.1016/j.atmosenv.2007.04.024.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Environment 41(29), 6116–6131 (2007) https://doi.org/10.1016/j.atmosenv.2007.04.024

Reference 36

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This paper cites Atmosphere11(5) (2020) https://doi.org/10.3390/atmos11050525.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmosphere11(5) (2020) https://doi.org/10.3390/atmos11050525

Reference 37

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This paper cites Atmospheric Environment241, 117752 (2020) https://doi.org/10.1016/j.atmosenv.2020.117752 30.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Environment241, 117752 (2020) https://doi.org/10.1016/j.atmosenv.2020.117752 30

Reference 38

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This paper cites Neural computation 9(8), 1735–1780 (1997).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Neural computation 9(8), 1735–1780 (1997)

Reference 39

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This paper cites Advances in neural information processing systems35, 26565–26577 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Advances in neural information processing systems35, 26565–26577 (2022)

Reference 40

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This paper cites Computer Methods in Applied Mechanics and Engineering435, 117623 (2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Computer Methods in Applied Mechanics and Engineering435, 117623 (2025)

Reference 41

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This paper cites Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 42

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Denoising Diffusion Implicit Models

Reference 43

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This paper cites IEEE transactions on image processing13(4), 600–612 (2004).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations IEEE transactions on image processing13(4), 600–612 (2004)

Reference 44

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Pearson Education, Upper Saddle River, NJ (2008)

Reference 45

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

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From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

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This paper cites ACM Siggraph Computer Graphics19(3), 287– 296 (1985).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ACM Siggraph Computer Graphics19(3), 287– 296 (1985)

Reference 48

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This paper cites In: Central European Seminar on Computer Graphics, vol.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: Central European Seminar on Computer Graphics, vol

Reference 49

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